APPENDIX C: DISTRIBUTIONAL ANALYSIS OF RURAL ELECTRIFICATION

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1 Intensive versus Extensive Growth in Electrification Coverage The rural electrification (RE) rate may be broken down as follows: ec P P e e P =, (C.1) ec P P P where P e are households with electricity, P ec the population of communities with electricity, and P the total population. The first term on the righthand side of the equation is the electrification rate in electrified communities and the second term is the share of the population of electrified communities in the total population (that is, the population-weighted proportion of electrified communities). Figure C.1 shows these three terms for four countries at two times using Demographic and Health Survey (DHS) data. The final bar in each set shows coverage of rural communities: the Philippines had achieved a high level of coverage (more than 80 percent of the population lived in communities that had electricity) in the first year shown (1993), and Bangladesh achieved a substantial increase in the proportion of the population living in electrified villages, from 52 to 90 percent, between 1992 and A closer examination of the change in the electrification rate can be made through a decomposition analysis. From equation C.1, the percentage change in the electrification rate can be decomposed as follows: ˆ ˆ ˆ ec P P e e P = + (C.2) ec, P P P where the ˆ denotes the percentage change in the ratio. For large changes there will also be a residual. These two terms may be interpreted as intensification and extension, respectively. That is, coverage may expand either by households in connected villages taking connections or by extending connections to new villages. Table C.1 summarizes the analysis of this decomposition. As might be expected, when the coverage rate is low, then the bulk of the increase comes from extension, meaning that there remains a sizeable number of unconnected households in communities with electricity. This was evident in figure C.1, looking at the ratio P e /P ce, where in Bangladesh and Nepal only about half of households in electrified communities have electricity. Only in the Philippines, where more than half the population lives in connected communities, does the majority of the increase in coverage come from intensification. Lorenz Curves Which households are connecting to the grid can be further analyzed using Lorenz curves. In the first set of curves presented here, households are ranked according to a wealth index, starting with the poorest. The curve plots the cumulative share of against the cumulative share of households (ranked by wealth). If this line lies below the 45 o line, then poorer households are less likely to have electricity. DHS data are used to show these curves for five countries (figure C.2), drawn for rural areas only. All graphs show the same two points: (1) access to the grid is bias toward the non-poor, and (2) the bias reduces over time (as the electrification rate increases) (Nepal is an exception). This reduction in bias is most marked in Ghana, where the government embarked on a program to bring 83

2 THE WELFARE IMPACT OF RURAL ELECTRIFICATION Figure C.1: Rural Electrification Rate Ratios Ratio P e /P P e /P ce P ce /P P e /P P e /P ce P ce /P P e /P P e /P ce P ce /P P e /P P e /P ce P ce /P Bangladesh Nepal Peru Philippines Earliest year Earliest year Note: P = total population, P ce = population of communities with electricity; P e = households with electricity. Table C.1: Decomposition of Changes in Rural Electrification Rate Electrification rate Latest Percent Earliest year increase Change in rate attributable to: Country Period year (%) (%) in rate Intensification Extension Residual Bangladesh Nepal Peru Philippines electricity to all districts. This pattern is also shown in table C.2, which reports the Gini coefficient (an inequality measure between 0 and 1, which would be 0 if grid access were equitably distributed). For the countries for which an energy survey is available, the Lorenz curve may also be calculated for electricity consumption. Because the better-off consume greater amounts of electricity, the distribution of consumption is more unequal than is shown by the dichotomous variable of connected or not (see figure 3.2 in the main report for the cases of Lao PDR and the Philippines). In one case (Sri Lanka) there are sufficient observations to draw separate curves for grid and offgrid connections (see figure C.3). The sample for this survey is not representative connected households are oversampled and survey weights are not available, so the general shape of the line is misleading (the true share of unconnected is understated, making the distribution look more 84

3 Figure C.2: Electrification Lorenz Curves (A) Bangladesh (B) Ghana a (C) Nepal (D) Peru (E) Philippines Access to electricity is biased toward the better off. But that bias reduces as electrification rates increase. a. Sources for this section are Ghana Livings Standards Survey and IEG

4 THE WELFARE IMPACT OF RURAL ELECTRIFICATION Table C.2: Gini Coefficients for Access to Electricity Country Year National Rural Bangladesh Ghana Ghana a 1989 n.a n.a. 0 Morocco Nepal Nicaragua Peru Philippines Senegal Nepal a. Source for this category is Ghana Living Standards Survey. Figure C.3: Lorenz Curve for Grid and Off-Grid Connections, Sri Lanka connected population Grid SHS 45 Source: IEG calculation from Sri Lanka household survey data. Note: SHS = solar home system. equal than it is), but the curves for grid and offgrid can be compared. These curves show that offgrid connections are less equitably distributed than are grid connections. Electricity Pricing and Subsidy Distribution Economists generally argue that efficiency is obtained through marginal cost pricing. However, for industries with large fixed costs, there may be increasing returns to scale, so that average costs fall as supply expands. Under these circumstances marginal cost pricing implies running at a loss, and in public economics this has long been taken as a case for state subsidy. Governments have also been keen to subsidize to ensure public utilities achieve good coverage; once established, subsidies become entrenched and politically difficult to remove. Fiscal pressures in developing countries have pushed some governments to seek greater levels of cost recovery, but electricity supply is still subsidized in virtually every country in the world. Foster and Yepes (2006) analyze cost structures to derive the following basic categorization: (1) tariffs below $4 per kilowatt hour (kwh) are insufficient to cover basic operations and management (O&M) costs for either residential or industrial customers; (2) tariffs above $5/kWh are sufficient to cover O&M and make a significant contribution toward capital costs for industrial users; and (3) tariffs above $8/kWh are sufficient to cover O&M and make a significant contribution toward capital costs for residential users. This categorization applies to most, though not all, gridbased distribution systems. Using these figures a regional classification can be produced, as shown in table C.3. There is a clear pattern showing that countries in poorer Regions are less likely to achieve cost recovery, with nearly a third of countries in East Asia and Pacific, Sub- Saharan Africa, and South Asia charging insufficient fees to cover O&M and virtually all the remainder unable to make a contribution toward capital costs. Who benefits from this subsidy clearly depends both on who uses electricity and how much they use and on the nature of the subsidy scheme. Subsidies may be either to the cost of connection or on the tariff. A linear tariff (that is, a fixed amount per kilowatt hour) will give the subsidy propor- 86

5 Table C.3: Percentage of Countries Covering O&M and Capital Costs through Electricity Charges Too low to cover Enough to cover Enough to cover basic O&M most O&M O&M and partial capital OECD (n = 23) Latin America and Caribbean (n = 19) Europe and Central Asia (n = 18) East Asia and Pacific (n = 13) Sub-Saharan Africa (n = 8) South Asia (n = 3) Global (n = 84) Source: Komives and others 2005, table 2.5. tional to consumption, or more than proportional if the tariff is a monthly fixed charge plus a linear tariff (because then the average charge per kilowatt hour falls as consumption rises). But the majority of electricity companies use differentiated tariffs. These tariffs take two basic forms. Most common is the increasing block tariff, whereby consumers pay more for units of consumption above a certain threshold. The alternative is a volumedifferentiated tariff, where those who consume above the threshold pay a higher rate on all consumption, not just that above the threshold. Komives and others (2005) present a comprehensive analysis of who benefits from electricity tariffs. The general finding is that these subsidies are poorly targeted, being less pro-poor than a simple random allocation of money among the population. The targeting measure used is ø, which is the ratio of the share of the subsidy going to the poor to their share in the population. Hence if ø = 1, the subsidy goes to the poor exactly in proportion to their population share (which is what a random allocation would achieve). In contrast, ø < 1 means the poor get a lower share of the subsidy than their population shares; that is, it disproportionately benefits the non-poor. Quantity-based tariff subsidies have an average ø of just 2, though this performance can be improved (to ø > 1) if combined with geographic targeting or means testing. The reason for this finding is clear. The poor are less likely to be connected (as shown in the analysis above) and so are excluded from the subsidy altogether. And the better-off consume more, so even if the tariff rises, as long as it is still subsidized, the non-poor may obtain a greater absolute benefit. Connection subsidies appear to be a different story, although few studies have been done. Because connection rates among the poor are low, even untargeted subsidies have ø > 1, with still higher values if combined with geographic targeting. 87